Evaluation of Factors Predictive of Efficacy Among Patients With Complicated Urinary Tract Infection and/or Acute Pyelonephritis

Abstract Background Antibiotic treatment for complicated urinary tract infections (cUTI)/acute pyelonephritis (AP) is often followed by recurrent bacteriuria in the absence of clinical symptoms. To understand factors predictive of clinical and microbiologic outcomes in patients with cUTI/AP, multivariable analyses were undertaken using pooled data from a global, phase 3 cUTI study. Methods Using data from 366 tebipenem pivoxil hydrobromide– and 378 ertapenem-treated patients from the Study to Assess the Efficacy, Safety and Pharmacokinetics of Orally Administered Tebipenem Pivoxil Hydrobromide (SPR994) Compared to Intravenous Ertapenem in Participants With Complicated Urinary Tract Infection (cUTI) or Acute Pyelonephritis (AP) infected with Enterobacterales uropathogens, multivariable analyses for dichotomous efficacy endpoints were performed using logistic regression and pharmacokinetic-pharmacodynamic relationships were evaluated. Results Urinary tract anatomical disorders and functional urinary tract or metabolic disorders were predictive of nonresponse across all efficacy endpoints assessed at test-of-cure (TOC) and late follow-up (LFU) visits, with greater impact on overall and microbiologic than clinical nonresponse. Independent variables predictive of increased probabilities of successful overall response at TOC and microbiologic response at TOC or LFU were baseline creatinine clearance >50 mL/min and baseline pathogen fluoroquinolone susceptibility. Infection with a phenotypic extended-spectrum beta-lactamase–positive Enterobacterales pathogen was predictive of reduced probabilities of success for microbiologic response at LFU and clinical response at TOC. Meaningful relationships between efficacy endpoints and plasma pharmacokinetic-pharmacodynamic indices were not identified. Conclusions Reductions of overall and microbiologic response in patients with cUTI/AP were associated with anatomical or functional urinary tract disorders, but not with the magnitude or duration of plasma antibiotic exposure. Results of these analyses serve to advance our understanding of factors predictive of outcome in patients with cUTI/AP.

The population PK model described by Lakota et al. was a linear three-compartment model with total body weight as a covariate on clearance and body surface area (BSA) as a covariate on central volume [1].This model had been developed using data from subjects with normal renal function, which resulted in a lack of a significant correlation between renal function and ertapenem.Given that ertapenem clearance is known to be lower in patients with reduced renal function [2], the published model was refined in order to include a relationship between ertapenem clearance and creatinine clearance (CLcr) normalized to BSA (mL/min/1.73m 2 ).This was accomplished by fitting a linear regression model to the observed clearance and CLcr values from Mistry et al. [2] (Supplementary Figure 1) and including the slope derived from that fit into the typical value equation for ertapenem clearance.The typical value equations for this refined population PK model are provided below: CL (L/h) = [1.71+ 0.01143 * (CLcr-110)] * (WTKG/95.9) 0.278   Vc (L) = 4.76 * (BSA/2.06) 1.86   CLd1 (L/h) = 6.71Vp1 (L) = 2.96 CLd2 (L/h) = 0.296 Vp2 (L) = 1.1 Where CL is the typical value of ertapenem clearance; CLcr is creatinine clearance in mL/min/1.73m 2 ; WTKG is body weight in kg; Vc is volume of the central compartment; BSA is body surface area in m 2 ; Vp1 is volume of the first peripheral compartment; CLd1 is the clearance into Vp1; Vp2 is volume of the second peripheral compartment; and CLd2 is the clearance into Vp2.The interindividual variability estimates for ertapenem CL and Vc/Vp1 were 9.57% and 7.92%, respectively.Note that the relationship between ertapenem CL and CLcr was modified from the regression equation in Supplementary Figure 1 to reflect the typical value of ertapenem CL in the subjects with normal renal function included in the dataset used to development the model from Lakota et al. [1].

Predictive Performance of the Population Pharmacokinetic Model for Subjects Without Ertapenem Concentration-Time Data
A validation exercise was performed to determine whether the ertapenem population PK model can be used to generate PK parameters and ertapenem exposures using dosing history and demographic information alone for patients in the absence of concentrationtime data.The goal of this exercise was to confirm that using the typical value of the PK parameters (i.e., without invoking random interindividual variability) is a valid approach for estimating exposure in patients for whom drug concentration data were unavailable.This exercise was carried out using R version 4.0.4[3].
For the first set of simulations, 687 ertapenem-treated patients with cUTI/AP from the ADAPT-PO study were replicated five times in order to generate a simulated population that contained 3,435 simulated patients.Individual PK parameters for ertapenem were calculated for each simulated patient using demographic values derived from the observed ADAPT-PO study population and the population PK model.First, typical PK values for each simulated patient were calculated using patient characteristics that were included as covariates in the population PK model.Individual PK parameter values for each simulated patient were then generated by applying an individual specific random effect (η) to each patient's typical PK value.Each simulated patient's η value was drawn from a log-normal distribution with a mean of zero and a variance derived from the population PK model for each parameter with associated IIV.Given that each patient in a given simulated population was assigned a set of PK parameters via Monte Carlo simulation, the individual patient replicates were subsequently considered to be distinct simulated patients.Ertapenem total-drug plasma concentration-time profiles from 0 to 24 hours were generated for each simulated patient based on the dosing history of the originally observed patients.Ertapenem free-drug plasma %T>MIC from 0 to 24 hours was determined for each simulated patient by counting the total number of free-drug concentrations that were above a given MIC value, multiplying this number by the time interval between simulated concentrations (0.1 hour), and then dividing this product by 24 hours.Ertapenem free-drug plasma %T>MIC was determined for fixed ertapenem MIC values based on the observed range of MIC values for the collections of Enterobacterales.These values were defined as the "true" free-drug plasma %T>MIC values (free-drug plasma %T>MICtrue).
For the second set of simulations, the same process was performed as the first set of simulations except that random effects were not implemented.Thus, the typical PK parameter value (e.g., population mean) was calculated for each simulated patient using only their specific demographic information in the population PK model.Ertapenem freedrug plasma %T>MIC were subsequently generated using the same process described for the first set of simulations.These values were defined as the "population mean" freedrug plasma %T>MIC values (free-drug plasma %T>MICpopulation mean).
The free-drug plasma %T>MICpopulation mean values were then compared to the free-drug plasma %T>MICtrue values for each simulated patient by calculating the percent predicted error (PE%) which is free-drug plasma %T>MICpopulation mean -free-drug plasma %T>MICtrue.Summary statistics of the percent predicted error (PE%) and absolute predicted error (|PE%|) were calculated and used to assess bias and precision of the %T>MICpopulation mean across MIC values ranging from 0.002 to 16 mg/L, respectively.As a general guideline, if the mean PE% was ± 5% and |PE%| was less than 10%, it was to be deemed that the population PK model for ertapenem can provide a reasonable estimation of free-drug plasma %T>MIC for the patients enrolled in the ADAPT-PO study who received ertapenem and had dosing history and demographic information available but did not have ertapenem PK data collected.bias and precision for the free-drug plasma %T>MIC by MIC value are provided in Supplementary Table 12.Ertapenem Free-Drug Plasma %T>MICpopulation mean is shown in Supplementary Figure 2. A box-plot of the distribution of PE% by MIC value (Supplementary Figure 3) showed that the bias was minimal (median PE% was 0%) across all MIC values.For MIC values ranging from 0.002 to 0.12 µg/mL, precision was high as indicated by the collapsed boxplots which is attributed to the concentration profile being entirely above the MIC threshold leading to free-drug plasma %T>MIC value of 100% for both estimated and true values.For MIC values ranging from 0.25 to 2 µg/mL, precision decreased as indicated by the wider boxplots but was still within the acceptable range of < 10% (median |PE%| was less than 4.15%).For MIC values ranging from 4 to 16 µg/mL, the precision increased as the median |PE%| was less than 1.25% which is attributed to the concentration profile being entirely below the MIC threshold leading to a free-drug plasma %T>MIC value of 0% for both estimated and true values.Therefore, the approach of using the typical values of the PK parameters from the model, as opposed to invoking randomly variability, was deemed acceptable for estimating ertapenem freedrug plasma %T>MIC values for use in the primary PK-PD analyses.The appropriateness of this approach is further justified based on the results of the sensitivity analyses described in the results section of the publication.

Table 1 .
Summary statistics for categorical baseline patient demographic and disease characteristics based on data from patients in the ME analysis population infected with Enterobacterales pathogen(s) at baseline, by treatment group and pooled

Table 1 .
Summary statistics for categorical baseline patient demographic and disease characteristics based on data from patients in the ME analysis population infected with Enterobacterales pathogen(s) at baseline, by treatment group and pooled Based on the selected analysis pathogen.c.ESBL resistance phenotype was defined as positive if ceftazidime MIC ≥ 2 µg/mL (or ceftriaxone MIC ≥ 2 µg/mL if ceftazidime susceptibility was not available).d.Fluoroquinolone resistance phenotype was defined as non-susceptible if levofloxacin MIC ≥ 1 µg/mL.e.TMP-SMX resistance phenotype was defined as non-susceptible if TMP-SMX MIC ≥ 4 µg/mL.f.Baseline risk factors include the following: age ≥ 65 years, cUTI, bacteremia at baseline, modified SIRS criteria at baseline, urinary tract anatomical disorders, functional urinary tract or metabolic disorders, or urinary tract instrumentation/procedure at baseline.
[4]Creatinine clearance (CLcr) was calculated by Cockcroft Gault equation using serum creatinine data collected at baseline from the local laboratory[4].b.

Table 2 .
[4]mary statistics for continuous baseline patient demographic and disease characteristics based on data from patients in the ME analysis population infected with Enterobacterales pathogen(s) at baseline, by treatment group and pooled Creatinine clearance (CLcr) was calculated by Cockcroft Gault equation using serum creatinine data collected at baseline from the local laboratory[4]. a.

Table 3 .
Listing of terms used to define urinary tract anatomical disorders, functional urinary tract or metabolic disorders, and urinary tract instrumentation/procedures

Table 3 .
Listing of terms used to define urinary tract anatomical disorders, functional urinary tract or metabolic disorders, and urinary tract instrumentation/procedures

Table 3 .
Listing of terms used to define urinary tract anatomical disorders, functional urinary tract or metabolic disorders, and urinary tract instrumentation/procedures

Table 4 .
Assessments of univariable associations between efficacy endpoints and candidate independent variables for the multivariable analyses

Table 7 .
Final multivariable logistic regression model for successful microbiologic response at TOC

Table 9 .
Selected associations with bacteremia and modified SIRS criteria

Table 10 .
Univariable and multivariable odds ratios for associations between efficacy endpoints and optimized two-group forms of tebipenem baseline MIC and Day 1 free-drug plasma AUC and AUC:MIC ratio•1/ : Shading indicates statistical significance at an individual 0.05 level. Note

Table 11 .
Univariable and multivariable odds ratios for associations between efficacy endpoints and optimized two-group forms of ertapenem baseline MIC and Day 1 free-drug plasma %T>MIC : Shading indicates statistical significance at an individual 0.05 level. Note